Viz_and_Eda
Dionna Attinson
9/26/2019
create the weather data
weather_df =
rnoaa:: meteo_pull_monitors(c("USW00094728", "USC00519397", "USS0023B17S"),
var = c("PRCP", "TMIN", "TMAX"),
date_min = "2017-01-01",
date_max = "2017-12-31") %>%
mutate(
name = recode(id, USW00094728 = "CentralPark_NY",
USC00519397 = "Waikiki_HA",
USS0023B17S = "Waterhole_WA"),
tmin = tmin / 10,
tmax = tmax / 10) %>%
select(name, id, everything())## Registered S3 method overwritten by 'crul':
## method from
## as.character.form_file httr## Registered S3 method overwritten by 'hoardr':
## method from
## print.cache_info httr## file path: /Users/dionna_attinson/Library/Caches/rnoaa/ghcnd/USW00094728.dly## file last updated: 2019-09-26 10:27:41## file min/max dates: 1869-01-01 / 2019-09-30## file path: /Users/dionna_attinson/Library/Caches/rnoaa/ghcnd/USC00519397.dly## file last updated: 2019-09-26 10:27:57## file min/max dates: 1965-01-01 / 2019-09-30## file path: /Users/dionna_attinson/Library/Caches/rnoaa/ghcnd/USS0023B17S.dly## file last updated: 2019-09-26 10:28:02## file min/max dates: 1999-09-01 / 2019-09-30weather_df## # A tibble: 1,095 x 6
## name id date prcp tmax tmin
## <chr> <chr> <date> <dbl> <dbl> <dbl>
## 1 CentralPark_NY USW00094728 2017-01-01 0 8.9 4.4
## 2 CentralPark_NY USW00094728 2017-01-02 53 5 2.8
## 3 CentralPark_NY USW00094728 2017-01-03 147 6.1 3.9
## 4 CentralPark_NY USW00094728 2017-01-04 0 11.1 1.1
## 5 CentralPark_NY USW00094728 2017-01-05 0 1.1 -2.7
## 6 CentralPark_NY USW00094728 2017-01-06 13 0.6 -3.8
## 7 CentralPark_NY USW00094728 2017-01-07 81 -3.2 -6.6
## 8 CentralPark_NY USW00094728 2017-01-08 0 -3.8 -8.8
## 9 CentralPark_NY USW00094728 2017-01-09 0 -4.9 -9.9
## 10 CentralPark_NY USW00094728 2017-01-10 0 7.8 -6
## # … with 1,085 more rowscreate a ggplot
ggplot(weather_df, aes(x= tmin, y= tmax)) +
geom_point()## Warning: Removed 15 rows containing missing values (geom_point).
alternate way of making this plot
weather_df %>%
ggplot(aes(x= tmin, y = tmax)) +
geom_point()## Warning: Removed 15 rows containing missing values (geom_point).
saving initial plots
weather_df %>% filter(name == "CentralPark_NY")## # A tibble: 365 x 6
## name id date prcp tmax tmin
## <chr> <chr> <date> <dbl> <dbl> <dbl>
## 1 CentralPark_NY USW00094728 2017-01-01 0 8.9 4.4
## 2 CentralPark_NY USW00094728 2017-01-02 53 5 2.8
## 3 CentralPark_NY USW00094728 2017-01-03 147 6.1 3.9
## 4 CentralPark_NY USW00094728 2017-01-04 0 11.1 1.1
## 5 CentralPark_NY USW00094728 2017-01-05 0 1.1 -2.7
## 6 CentralPark_NY USW00094728 2017-01-06 13 0.6 -3.8
## 7 CentralPark_NY USW00094728 2017-01-07 81 -3.2 -6.6
## 8 CentralPark_NY USW00094728 2017-01-08 0 -3.8 -8.8
## 9 CentralPark_NY USW00094728 2017-01-09 0 -4.9 -9.9
## 10 CentralPark_NY USW00094728 2017-01-10 0 7.8 -6
## # … with 355 more rowsscatterplot=
weather_df %>%
ggplot(aes(x= tmin, y= tmax)) +
geom_point()
scatterplot## Warning: Removed 15 rows containing missing values (geom_point).
adding color…
weather_df %>%
ggplot(aes(x= tmin, y= tmax)) +
geom_point(aes(color=name), alpha = .4)## Warning: Removed 15 rows containing missing values (geom_point).
vs
weather_df %>%
ggplot(aes(x = tmin, y= tmax, color = name)) +
geom_point(aes(color=name), alpha = .5) +
geom_smooth(se=FALSE)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning: Removed 15 rows containing non-finite values (stat_smooth).## Warning: Removed 15 rows containing missing values (geom_point).
facet!
ggplot(weather_df, aes(x = tmin, y = tmax, color = name)) +
geom_point(alpha = .5) +
geom_smooth(se = FALSE) +
facet_grid(. ~ name)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning: Removed 15 rows containing non-finite values (stat_smooth).## Warning: Removed 15 rows containing missing values (geom_point).
this is fine but not interesting
weather_df %>%
ggplot(aes(x = date, y = tmax, color = name)) +
geom_point(aes(size=prcp), alpha = .5) +
geom_smooth(size = 2, se = FALSE)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning: Removed 3 rows containing non-finite values (stat_smooth).## Warning: Removed 3 rows containing missing values (geom_point).
Some extra stuff
weather_df %>%
ggplot(aes(x = date, y = tmax, color = name)) +
geom_smooth(size = 2, se = FALSE)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning: Removed 3 rows containing non-finite values (stat_smooth).
2d density
weather_df %>%
ggplot(aes(x = tmin, y = tmax)) +
geom_bin2d() ## Warning: Removed 15 rows containing non-finite values (stat_bin2d).
More kinds of plots!!
Understand the distribution of a single variable
weather_df %>%
ggplot(aes(x = tmax, fill = name)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 3 rows containing non-finite values (stat_bin).
weather_df %>%
ggplot(aes(x = tmax, fill = name)) +
geom_histogram(position = "dodge")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 3 rows containing non-finite values (stat_bin).
weather_df %>%
ggplot(aes(x = tmax, fill = name)) +
geom_histogram(position = "dodge") +
facet_grid(~name)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 3 rows containing non-finite values (stat_bin).
A Density Plot
weather_df %>%
ggplot(aes(x = tmax, fill = name)) +
geom_density(alpha = .3) ## Warning: Removed 3 rows containing non-finite values (stat_density).
A Box Plot
weather_df %>%
ggplot(aes(x = name, y = tmax)) +
geom_boxplot()## Warning: Removed 3 rows containing non-finite values (stat_boxplot).
weather_df %>%
ggplot(aes(x = name, y = tmax)) +
geom_violin()## Warning: Removed 3 rows containing non-finite values (stat_ydensity).
Ridge plots!!
weather_df %>%
ggplot(aes(x= tmax, y= name)) +
geom_density_ridges()## Picking joint bandwidth of 1.84## Warning: Removed 3 rows containing non-finite values (stat_density_ridges).
##saving a plot
ggp_ridge_temp =
weather_df %>%
ggplot(aes(x = tmax, y = name)) +
geom_density_ridges()
ggsave("ggplot_temp_ridge.pdf", ggp_ridge_temp)## Saving 7 x 5 in image## Picking joint bandwidth of 1.84## Warning: Removed 3 rows containing non-finite values (stat_density_ridges).weather_df %>%
ggplot(aes(x = tmin, y = tmax, color = name)) +
geom_point(alpha = .4) +
geom_smooth(se = FALSE)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning: Removed 15 rows containing non-finite values (stat_smooth).## Warning: Removed 15 rows containing missing values (geom_point).
weather_df %>%
ggplot(aes(x = tmin, y = tmax, color = name)) +
geom_point(alpha = .4) +
geom_smooth(se = FALSE)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning: Removed 15 rows containing non-finite values (stat_smooth).## Warning: Removed 15 rows containing missing values (geom_point).
weather_df %>%
ggplot(aes(x=tmin, y=tmax, color=name)) +
geom_point(alpha = .5) +
labs(
title = "temperature plot",
x = "minimum temp (c)",
y = "maximum temp (c)",
caption = "Data from NOAA via rnoaa package"
) +
scale_x_continuous(
breaks = c(-15, 0, 15),
labels = c("-15º C", "0", "15")
) +
scale_y_continuous(
trans = "sqrt",
position = "right")## Warning in self$trans$transform(x): NaNs produced## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 90 rows containing missing values (geom_point).
Color themes produced
ggp_base =
weather_df %>%
ggplot(aes(x = tmin, y = tmax)) +
geom_point(aes(color = name), alpha = .5) +
labs(
title = "Temperature plot",
x = "Minimum daily temperature (C)",
y = "Maxiumum daily temperature (C)",
caption = "Data from the rnoaa package"
) +
viridis::scale_color_viridis(
name = "Location",
discrete = TRUE)
ggp_base## Warning: Removed 15 rows containing missing values (geom_point).
Other themes
ggp_base +
theme_minimal() +
theme(legend.position="bottom")## Warning: Removed 15 rows containing missing values (geom_point).
Where do my warnings come from…
weather_df %>%
filter(is.na(tmin))## # A tibble: 15 x 6
## name id date prcp tmax tmin
## <chr> <chr> <date> <dbl> <dbl> <dbl>
## 1 Waikiki_HA USC00519397 2017-04-17 5 28.3 NA
## 2 Waikiki_HA USC00519397 2017-05-09 NA NA NA
## 3 Waikiki_HA USC00519397 2017-05-26 NA NA NA
## 4 Waikiki_HA USC00519397 2017-07-19 NA NA NA
## 5 Waikiki_HA USC00519397 2017-10-07 0 31.1 NA
## 6 Waikiki_HA USC00519397 2017-10-09 0 28.9 NA
## 7 Waikiki_HA USC00519397 2017-10-10 10 31.7 NA
## 8 Waikiki_HA USC00519397 2017-10-12 0 31.1 NA
## 9 Waikiki_HA USC00519397 2017-10-13 0 31.1 NA
## 10 Waikiki_HA USC00519397 2017-10-16 5 30 NA
## 11 Waikiki_HA USC00519397 2017-10-18 0 29.4 NA
## 12 Waikiki_HA USC00519397 2017-10-20 13 30.6 NA
## 13 Waikiki_HA USC00519397 2017-10-21 0 30 NA
## 14 Waikiki_HA USC00519397 2017-10-22 0 30 NA
## 15 Waikiki_HA USC00519397 2017-12-22 0 26.7 NAmore than one dataset
central_park =
weather_df %>%
filter(name == "CentralPark_NY")
waikiki =
weather_df %>%
filter(name== "Waikiki_HA")
ggplot(data = waikiki, aes(x = date, y = tmax, color = name)) +
geom_point() +
geom_line(data = central_park)## Warning: Removed 3 rows containing missing values (geom_point).
Multi-panel Plots
# install.packages("devtools")
devtools::install_github("thomasp85/patchwork")## Skipping install of 'patchwork' from a github remote, the SHA1 (36b49187) has not changed since last install.
## Use `force = TRUE` to force installationggp_scatter =
weather_df %>%
ggplot(aes(x = tmin, y = tmax)) +
geom_point()
ggp_density =
weather_df %>%
ggplot(aes(x = tmin))+
geom_density()
ggp_box =
weather_df %>%
ggplot(aes(x = name, y = tmax)) +
geom_boxplot()
(ggp_scatter + ggp_density) /ggp_box## Warning: Removed 15 rows containing missing values (geom_point).## Warning: Removed 15 rows containing non-finite values (stat_density).## Warning: Removed 3 rows containing non-finite values (stat_boxplot).
Data Manipulation
reorder function organizes data by size
weather_df %>%
mutate(
name = factor(name),
name = fct_reorder(name, tmax)
) %>%
ggplot(aes(x = name, y = tmax, color = name)) +
geom_boxplot()## Warning: Removed 3 rows containing non-finite values (stat_boxplot).
pup_data =
read_csv("./data/FAS_pups.csv", col_types = "ciiiii") %>%
janitor::clean_names() %>%
mutate(sex = recode(sex, `1` = "male", `2` = "female"))
litter_data =
read_csv("./data/FAS_litters.csv", col_types = "ccddiiii") %>%
janitor::clean_names() %>%
select(-pups_survive) %>%
separate(group, into = c("dose", "day_of_tx"), sep = 3) %>%
mutate(wt_gain = gd18_weight - gd0_weight,
day_of_tx = as.numeric(day_of_tx))
fas_data = left_join(pup_data, litter_data, by = "litter_number")
fas_data %>%
select(sex, dose, day_of_tx, pd_ears:pd_walk) %>%
pivot_longer(
pd_ears:pd_walk,
names_to = "outcome",
values_to = "pn_day") %>%
mutate(
outcome = factor (outcome),
outcome = fct_reorder (outcome, pn_day)
) %>%
drop_na(dose, day_of_tx, pn_day) %>%
mutate(outcome = forcats::fct_reorder(outcome, day_of_tx, median)) %>%
ggplot(aes(x = dose, y = pn_day)) +
geom_violin() +
facet_grid(day_of_tx ~ outcome)